Inferential statistics is used to make inferences (decisions, estimates, predictions, or generalizations) about a population of measurements based on information contained in a sample of those measurements. The two basic types of statistical inference are estimation and hypothesis testing. The goal of estimation is to estimate a value for an unknown population characteristic (parameter) based on a sample statistic. Both point estimators and interval estimators can be used for this task. Hypothesis testing, which is more involved than estimation, consists of six steps constituting a formal procedure whose goal is to draw inferences or conclusions about the value of one or more population parameters based on sample statistics estimating those parameters. Uncertainty is inherent in both estimation and hypothesis testing since statistics can vary from one sample to another. A sampling distribution depicts the probability of occurrence of all values a statistic can assume when computed for all possible independent, random samples of size n. Sampling distributions are used to evaluate the reliability of inferences made using statistics.